Table 1.

Comparison of denoising performance between BM3D filter and deep learning models.

Model	Evaluation metrics
	PSNR	SSIM
BM3D	$31.04\pm 5.86$	$0.649\pm 0.207$
DN	$32.05\pm 6.02$	$0.671\pm 0.220$
N2N	$\mathbf{32.21}\pm \mathbf{6.04}$	$\mathbf{0.679}\pm \mathbf{0.214}$
W5	$31.96\pm 6.02$	$0.668\pm 0.221$

Paired t-test	P-value
	PSNR	SSIM
N2N vs BM3D	0.045*	0.016*
N2N vs DN	0.0059*	0.027*
N2N vs W5	0.00035*	0.016*

The average and standard deviation of each metric over five test images are shown. The N2N shows the significant difference from the others in a paired t-test ($n=5$, $\mathrm{P}<0.05$) using the Bonferroni–Holm correction. N2N showed the highest performance for both metrics. * means significant difference after the Bonferroni–Holm correction ($\alpha <0.05$).